Ignorability: The missing data mechanism is said to be ignorable for likelihood inference if (1) the joint likelihood for the responses of the interest and missing data indicators can be decomposed into the two separate components (containing parameters of the main interest and the parameters of the missingness mechanism,) and (2) the parameters for each component are distinct in the sense that there are no parameter restrictions across components. The component for the missingness mechanism can then be unnoticed in statistical inference for the parameters of interest. Ignorability follows if the missing values are missing completely at random or missing at random and the parameters are distinct.